On Uniformly Optimal Networks: A Reversal of Fortune?

The case of Pinochet has aroused enormous interest, both political and legal. The spectacle of the General, whose regime sent so many to their deaths, himself under arrest and standing trial has stirred the hopes of the oppressed. His reversal of fortune, loss of liberty with a policeman, on the door, has been heralded by organisations for the protection of human rights as one small step on the long road to justice. For lawyers generally, the House of Lords' majority decision of 1998 that General Pinochet enjoyed no immunity signalled a shift from a State-centred order of things.1 It suggested that the process of restriction of State immunity, so effectively begun with the removal of commercial transactions from its protection, might now extend some way into the field of criminal proceedings. And it further posed the intriguing question whether an act categorised as within the exercise of sovereign power, so as to relieve the individual official of liability in civil proceedings, may at the same time, as well as subsequent to his retirement, attract parallel personal criminal liability.

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Equilibrium Provider Networks: Bargaining and Exclusion in Health Care Markets

The American Economic Review ◽

10.1257/aer.20171288 ◽

2019 ◽

Vol 109 (2) ◽

pp. 473-522 ◽

Cited By ~ 15

Author(s):

Kate Ho ◽

Robin S. Lee

Keyword(s):

Health Care ◽

Consumer Welfare ◽

Bargaining Solution ◽

Health Care Markets ◽

Social Incentives ◽

Provider Networks ◽

Hospital Networks ◽

Private Incentives ◽

Social Surplus ◽

Optimal Networks

We evaluate the consequences of narrow hospital networks in commercial health care markets. We develop a bargaining solution, “Nash-in-Nash with Threat of Replacement,” that captures insurers’ incentives to exclude, and combine it with California data and estimates from Ho and Lee (2017) to simulate equilibrium outcomes under social, consumer, and insurer-optimal networks. Private incentives to exclude generally exceed social incentives, as the insurer benefits from substantially lower negotiated hospital rates. Regulation prohibiting exclusion increases prices and premiums and lowers consumer welfare without significantly affecting social surplus. However, regulation may prevent harm to consumers living close to excluded hospitals. (JEL C78, D85, G22, H75, I11, I13, I18)

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Reversal of Fortune in a Small, Open Economy: Regional GDP in Belgium, 1896-2000

SSRN Electronic Journal ◽

10.2139/ssrn.1586762 ◽

2009 ◽

Cited By ~ 3

Author(s):

Erik Buyst

Keyword(s):

Open Economy ◽

Small Open Economy ◽

Reversal Of Fortune

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DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

10.1101/2020.07.17.208918 ◽

2020 ◽

Author(s):

Pablo Rodríguez-Mier ◽

Nathalie Poupin ◽

Carlo de Blasio ◽

Laurent Le Cam ◽

Fabien Jourdan

Keyword(s):

Metabolic Network ◽

Metabolic Networks ◽

Relevant Information ◽

Experimental Information ◽

Correct Identification ◽

Good Representation ◽

Metabolic State ◽

Whole Space ◽

Context Specific ◽

Optimal Networks

AbstractThe correct identification of metabolic activity in tissues or cells under different environmental or genetic conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific organism and condition, containing only the reactions predicted to be active in such context. A major limitation of this approach is that the available information does not generally allow for an unambiguous characterization of the corresponding optimal metabolic sub-network, i.e., there are usually many different sub-network that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic state. In this work, we formalize the problem of enumeration of optimal metabolic networks, we implement a set of techniques that can be used to enumerate optimal networks, and we introduce DEXOM, a novel strategy for diversity-based extraction of optimal metabolic networks. Instead of enumerating the whole space of optimal metabolic networks, which can be computationally intractable, DEXOM samples solutions from the set of optimal metabolic sub-networks maximizing diversity in order to obtain a good representation of the possible metabolic state. We evaluate the solution diversity of the different techniques using simulated and real datasets, and we show how this method can be used to improve in-silico gene essentiality predictions in Saccharomyces Cerevisiae using diversity-based metabolic network ensembles. Both the code and the data used for this research are publicly available on GitHub1.

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